504 citations · 1.8k across the 20 of their papers we have counts for
20 papers
Prototype-Based Low Altitude UAV Semantic Segmentation
Da Zhang, Gao Junyu, Zhao Zhiyuan
Semantic segmentation of low-altitude UAV imagery presents unique challenges due to extreme scale variations, complex object boundaries, and limited computational resources on edge…
VisDrone-CC2020: The Vision Meets Drone Crowd Counting Challenge Results
Dawei Du, Longyin Wen, Pengfei Zhu +52
Crowd counting on the drone platform is an interesting topic in computer vision, which brings new challenges such as small object inference, background clutter and wide viewpoint.…
Learning Independent Instance Maps for Crowd Localization
Junyu Gao, Tao Han, Qi Wang +2
Accurately locating each head's position in the crowd scenes is a crucial task in the field of crowd analysis. However, traditional density-based methods only predict coarse predic…
Unsupervised Semantic Aggregation and Deformable Template Matching for Semi-Supervised Learning
Tao Han, Junyu Gao, Yuan Yuan +1
Unlabeled data learning has attracted considerable attention recently. However, it is still elusive to extract the expected high-level semantic feature with mere unsupervised learn…
A Flow Base Bi-path Network for Cross-scene Video Crowd Understanding in Aerial View
Zhiyuan Zhao, Tao Han, Junyu Gao +2
Drones shooting can be applied in dynamic traffic monitoring, object detecting and tracking, and other vision tasks. The variability of the shooting location adds some intractable…
Pixel-wise Crowd Understanding via Synthetic Data
Qi Wang, Junyu Gao, Wei Lin +1
Crowd analysis via computer vision techniques is an important topic in the field of video surveillance, which has wide-spread applications including crowd monitoring, public safety…
Ambient Sound Helps: Audiovisual Crowd Counting in Extreme Conditions
Di Hu, Lichao Mou, Qingzhong Wang +4
Visual crowd counting has been recently studied as a way to enable people counting in crowd scenes from images. Albeit successful, vision-based crowd counting approaches could fail…
Neuron Linear Transformation: Modeling the Domain Shift for Crowd Counting
Qi Wang, Tao Han, Junyu Gao +1
Cross-domain crowd counting (CDCC) is a hot topic due to its importance in public safety. The purpose of CDCC is to alleviate the domain shift between the source and target domain.…
CNN-based Density Estimation and Crowd Counting: A Survey
Guangshuai Gao, Junyu Gao, Qingjie Liu +2
Accurately estimating the number of objects in a single image is a challenging yet meaningful task and has been applied in many applications such as urban planning and public safet…
Pixel-Level Self-Paced Learning for Super-Resolution
Wei. Lin, Junyu. Gao, Qi. Wang +1
Recently, lots of deep networks are proposed to improve the quality of predicted super-resolution (SR) images, due to its widespread use in several image-based fields. However, wit…
Focus on Semantic Consistency for Cross-domain Crowd Understanding
Tao Han, Junyu Gao, Yuan Yuan +1
For pixel-level crowd understanding, it is time-consuming and laborious in data collection and annotation. Some domain adaptation algorithms try to liberate it by training models w…
NWPU-Crowd: A Large-Scale Benchmark for Crowd Counting and Localization
Qi Wang, Junyu Gao, Wei Lin +1
In the last decade, crowd counting and localization attract much attention of researchers due to its wide-spread applications, including crowd monitoring, public safety, space desi…
Feature-aware Adaptation and Density Alignment for Crowd Counting in Video Surveillance
Junyu Gao, Yuan Yuan, Qi Wang
With the development of deep neural networks, the performance of crowd counting and pixel-wise density estimation are continually being refreshed. Despite this, there are still two…
SCAR: Spatial-/Channel-wise Attention Regression Networks for Crowd Counting
Junyu Gao, Qi Wang, Yuan Yuan
Recently, crowd counting is a hot topic in crowd analysis. Many CNN-based counting algorithms attain good performance. However, these methods only focus on the local appearance fea…
C^3 Framework: An Open-source PyTorch Code for Crowd Counting
Junyu Gao, Wei Lin, Bin Zhao +3
This technical report attempts to provide efficient and solid kits addressed on the field of crowd counting, which is denoted as Crowd Counting Code Framework (CF). The contrib…
PCC Net: Perspective Crowd Counting via Spatial Convolutional Network
Junyu Gao, Qi Wang, Xuelong Li
Crowd counting from a single image is a challenging task due to high appearance similarity, perspective changes and severe congestion. Many methods only focus on the local appearan…
Embedding Structured Contour and Location Prior in Siamesed Fully Convolutional Networks for Road Detection
Qi Wang, Junyu Gao, Yuan Yuan
Road detection from the perspective of moving vehicles is a challenging issue in autonomous driving. Recently, many deep learning methods spring up for this task because they can e…
A Joint Convolutional Neural Networks and Context Transfer for Street Scenes Labeling
Qi Wang, Junyu Gao, Yuan Yuan
Street scene understanding is an essential task for autonomous driving. One important step towards this direction is scene labeling, which annotates each pixel in the images with a…
Weakly Supervised Adversarial Domain Adaptation for Semantic Segmentation in Urban Scenes
Qi Wang, Junyu Gao, Xuelong Li
Semantic segmentation, a pixel-level vision task, is developed rapidly by using convolutional neural networks (CNNs). Training CNNs requires a large amount of labeled data, but man…
Learning from Synthetic Data for Crowd Counting in the Wild
Qi Wang, Junyu Gao, Wei Lin +1
Recently, counting the number of people for crowd scenes is a hot topic because of its widespread applications (e.g. video surveillance, public security). It is a difficult task in…